Overview

Dataset statistics

Number of variables24
Number of observations85122
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.3 MiB
Average record size in memory224.8 B

Variable types

Numeric20
Categorical4

Alerts

date has a high cardinality: 1361 distinct valuesHigh cardinality
id_x is highly overall correlated with country_id and 1 other fieldsHigh correlation
match_api_id is highly overall correlated with seasonHigh correlation
B365H is highly overall correlated with BWH and 5 other fieldsHigh correlation
BWH is highly overall correlated with B365H and 5 other fieldsHigh correlation
IWH is highly overall correlated with B365H and 5 other fieldsHigh correlation
LBH is highly overall correlated with B365H and 5 other fieldsHigh correlation
WHH is highly overall correlated with B365H and 5 other fieldsHigh correlation
VCH is highly overall correlated with B365H and 5 other fieldsHigh correlation
avgOdds is highly overall correlated with B365H and 5 other fieldsHigh correlation
country_id is highly overall correlated with id_x and 1 other fieldsHigh correlation
league_id is highly overall correlated with id_x and 1 other fieldsHigh correlation
season is highly overall correlated with match_api_idHigh correlation
homeTeamID is highly skewed (γ1 = 27.14622012)Skewed
away_team_api_id is highly skewed (γ1 = 21.85942501)Skewed

Reproduction

Analysis started2023-02-28 23:46:01.442867
Analysis finished2023-02-28 23:46:57.510588
Duration56.07 seconds
Software versionpandas-profiling vv3.6.3
Download configurationconfig.json

Variables

id_x
Real number (ℝ)

Distinct14503
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10708.051
Minimum1729
Maximum24557
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:46:57.609611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1729
5-th percentile2439
Q15312
median8967
Q312652
95-th percentile23815
Maximum24557
Range22828
Interquartile range (IQR)7340

Descriptive statistics

Standard deviation7005.7292
Coefficient of variation (CV)0.65424879
Kurtosis-0.59839634
Mean10708.051
Median Absolute Deviation (MAD)3671
Skewness0.82642879
Sum9.1149069 × 108
Variance49080242
MonotonicityNot monotonic
2023-02-28T18:46:58.126729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1729 6
 
< 0.1%
10476 6
 
< 0.1%
13250 6
 
< 0.1%
13270 6
 
< 0.1%
10263 6
 
< 0.1%
10288 6
 
< 0.1%
10312 6
 
< 0.1%
10331 6
 
< 0.1%
10349 6
 
< 0.1%
10387 6
 
< 0.1%
Other values (14493) 85062
99.9%
ValueCountFrequency (%)
1729 6
< 0.1%
1730 6
< 0.1%
1731 6
< 0.1%
1732 6
< 0.1%
1733 6
< 0.1%
1734 6
< 0.1%
1735 6
< 0.1%
1736 6
< 0.1%
1737 6
< 0.1%
1738 6
< 0.1%
ValueCountFrequency (%)
24557 6
< 0.1%
24556 5
< 0.1%
24555 6
< 0.1%
24554 6
< 0.1%
24553 6
< 0.1%
24552 6
< 0.1%
24551 6
< 0.1%
24550 6
< 0.1%
24549 6
< 0.1%
24548 6
< 0.1%

country_id
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1729
18216 
21518
17786 
4769
17479 
10257
17210 
7809
14431 

Length

Max length5
Median length4
Mean length4.4111276
Min length4

Characters and Unicode

Total characters375484
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1729
2nd row1729
3rd row1729
4th row1729
5th row1729

Common Values

ValueCountFrequency (%)
1729 18216
21.4%
21518 17786
20.9%
4769 17479
20.5%
10257 17210
20.2%
7809 14431
17.0%

Length

2023-02-28T18:46:58.231752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T18:46:58.349779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1729 18216
21.4%
21518 17786
20.9%
4769 17479
20.5%
10257 17210
20.2%
7809 14431
17.0%

Most occurring characters

ValueCountFrequency (%)
1 70998
18.9%
7 67336
17.9%
2 53212
14.2%
9 50126
13.3%
5 34996
9.3%
8 32217
8.6%
0 31641
8.4%
4 17479
 
4.7%
6 17479
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 375484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 70998
18.9%
7 67336
17.9%
2 53212
14.2%
9 50126
13.3%
5 34996
9.3%
8 32217
8.6%
0 31641
8.4%
4 17479
 
4.7%
6 17479
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 375484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 70998
18.9%
7 67336
17.9%
2 53212
14.2%
9 50126
13.3%
5 34996
9.3%
8 32217
8.6%
0 31641
8.4%
4 17479
 
4.7%
6 17479
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 375484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 70998
18.9%
7 67336
17.9%
2 53212
14.2%
9 50126
13.3%
5 34996
9.3%
8 32217
8.6%
0 31641
8.4%
4 17479
 
4.7%
6 17479
 
4.7%

league_id
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1729
18216 
21518
17786 
4769
17479 
10257
17210 
7809
14431 

Length

Max length5
Median length4
Mean length4.4111276
Min length4

Characters and Unicode

Total characters375484
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1729
2nd row1729
3rd row1729
4th row1729
5th row1729

Common Values

ValueCountFrequency (%)
1729 18216
21.4%
21518 17786
20.9%
4769 17479
20.5%
10257 17210
20.2%
7809 14431
17.0%

Length

2023-02-28T18:46:58.451802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T18:46:58.555825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1729 18216
21.4%
21518 17786
20.9%
4769 17479
20.5%
10257 17210
20.2%
7809 14431
17.0%

Most occurring characters

ValueCountFrequency (%)
1 70998
18.9%
7 67336
17.9%
2 53212
14.2%
9 50126
13.3%
5 34996
9.3%
8 32217
8.6%
0 31641
8.4%
4 17479
 
4.7%
6 17479
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 375484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 70998
18.9%
7 67336
17.9%
2 53212
14.2%
9 50126
13.3%
5 34996
9.3%
8 32217
8.6%
0 31641
8.4%
4 17479
 
4.7%
6 17479
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 375484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 70998
18.9%
7 67336
17.9%
2 53212
14.2%
9 50126
13.3%
5 34996
9.3%
8 32217
8.6%
0 31641
8.4%
4 17479
 
4.7%
6 17479
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 375484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 70998
18.9%
7 67336
17.9%
2 53212
14.2%
9 50126
13.3%
5 34996
9.3%
8 32217
8.6%
0 31641
8.4%
4 17479
 
4.7%
6 17479
 
4.7%

season
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2013/2014
10760 
2010/2011
10753 
2012/2013
10751 
2014/2015
10644 
2011/2012
10640 
Other values (3)
31574 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters766098
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2008/2009
2nd row2008/2009
3rd row2008/2009
4th row2008/2009
5th row2008/2009

Common Values

ValueCountFrequency (%)
2013/2014 10760
12.6%
2010/2011 10753
12.6%
2012/2013 10751
12.6%
2014/2015 10644
12.5%
2011/2012 10640
12.5%
2008/2009 10586
12.4%
2009/2010 10575
12.4%
2015/2016 10413
12.2%

Length

2023-02-28T18:46:58.653847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T18:46:58.764872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2013/2014 10760
12.6%
2010/2011 10753
12.6%
2012/2013 10751
12.6%
2014/2015 10644
12.5%
2011/2012 10640
12.5%
2008/2009 10586
12.4%
2009/2010 10575
12.4%
2015/2016 10413
12.2%

Most occurring characters

ValueCountFrequency (%)
0 223319
29.2%
2 191635
25.0%
1 159890
20.9%
/ 85122
 
11.1%
3 21511
 
2.8%
4 21404
 
2.8%
9 21161
 
2.8%
5 21057
 
2.7%
8 10586
 
1.4%
6 10413
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 680976
88.9%
Other Punctuation 85122
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 223319
32.8%
2 191635
28.1%
1 159890
23.5%
3 21511
 
3.2%
4 21404
 
3.1%
9 21161
 
3.1%
5 21057
 
3.1%
8 10586
 
1.6%
6 10413
 
1.5%
Other Punctuation
ValueCountFrequency (%)
/ 85122
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 766098
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 223319
29.2%
2 191635
25.0%
1 159890
20.9%
/ 85122
 
11.1%
3 21511
 
2.8%
4 21404
 
2.8%
9 21161
 
2.8%
5 21057
 
2.7%
8 10586
 
1.4%
6 10413
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 766098
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 223319
29.2%
2 191635
25.0%
1 159890
20.9%
/ 85122
 
11.1%
3 21511
 
2.8%
4 21404
 
2.8%
9 21161
 
2.8%
5 21057
 
2.7%
8 10586
 
1.4%
6 10413
 
1.4%

date
Categorical

Distinct1361
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2012-05-13 00:00:00
 
221
2012-04-07 00:00:00
 
210
2013-03-30 00:00:00
 
208
2015-04-04 00:00:00
 
200
2010-04-03 00:00:00
 
194
Other values (1356)
84089 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1617318
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2008-08-17 00:00:00
2nd row2008-08-17 00:00:00
3rd row2008-08-17 00:00:00
4th row2008-08-17 00:00:00
5th row2008-08-17 00:00:00

Common Values

ValueCountFrequency (%)
2012-05-13 00:00:00 221
 
0.3%
2012-04-07 00:00:00 210
 
0.2%
2013-03-30 00:00:00 208
 
0.2%
2015-04-04 00:00:00 200
 
0.2%
2010-04-03 00:00:00 194
 
0.2%
2009-04-11 00:00:00 192
 
0.2%
2011-05-07 00:00:00 191
 
0.2%
2008-10-29 00:00:00 183
 
0.2%
2015-05-23 00:00:00 180
 
0.2%
2011-02-05 00:00:00 180
 
0.2%
Other values (1351) 83163
97.7%

Length

2023-02-28T18:46:58.882899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00 85122
50.0%
2012-05-13 221
 
0.1%
2012-04-07 210
 
0.1%
2013-03-30 208
 
0.1%
2015-04-04 200
 
0.1%
2010-04-03 194
 
0.1%
2009-04-11 192
 
0.1%
2011-05-07 191
 
0.1%
2008-10-29 183
 
0.1%
2015-05-23 180
 
0.1%
Other values (1352) 83343
49.0%

Most occurring characters

ValueCountFrequency (%)
0 721808
44.6%
- 170244
 
10.5%
: 170244
 
10.5%
1 161743
 
10.0%
2 152302
 
9.4%
85122
 
5.3%
3 33030
 
2.0%
4 28640
 
1.8%
9 26945
 
1.7%
5 26285
 
1.6%
Other values (3) 40955
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1191708
73.7%
Dash Punctuation 170244
 
10.5%
Other Punctuation 170244
 
10.5%
Space Separator 85122
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 721808
60.6%
1 161743
 
13.6%
2 152302
 
12.8%
3 33030
 
2.8%
4 28640
 
2.4%
9 26945
 
2.3%
5 26285
 
2.2%
8 18871
 
1.6%
6 14029
 
1.2%
7 8055
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
- 170244
100.0%
Other Punctuation
ValueCountFrequency (%)
: 170244
100.0%
Space Separator
ValueCountFrequency (%)
85122
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1617318
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 721808
44.6%
- 170244
 
10.5%
: 170244
 
10.5%
1 161743
 
10.0%
2 152302
 
9.4%
85122
 
5.3%
3 33030
 
2.0%
4 28640
 
1.8%
9 26945
 
1.7%
5 26285
 
1.6%
Other values (3) 40955
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1617318
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 721808
44.6%
- 170244
 
10.5%
: 170244
 
10.5%
1 161743
 
10.0%
2 152302
 
9.4%
85122
 
5.3%
3 33030
 
2.0%
4 28640
 
1.8%
9 26945
 
1.7%
5 26285
 
1.6%
Other values (3) 40955
 
2.5%

match_api_id
Real number (ℝ)

Distinct14503
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1196854.3
Minimum483129
Maximum2118418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:46:58.984922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum483129
5-th percentile489392
Q1829888.25
median1216804
Q31536862
95-th percentile2030088
Maximum2118418
Range1635289
Interquartile range (IQR)706973.75

Descriptive statistics

Standard deviation491767.11
Coefficient of variation (CV)0.41088303
Kurtosis-1.1730164
Mean1196854.3
Median Absolute Deviation (MAD)386875
Skewness0.23275169
Sum1.0187863 × 1011
Variance2.4183489 × 1011
MonotonicityNot monotonic
2023-02-28T18:46:59.107949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
489042 6
 
< 0.1%
539848 6
 
< 0.1%
2060390 6
 
< 0.1%
2060430 6
 
< 0.1%
537638 6
 
< 0.1%
539666 6
 
< 0.1%
539692 6
 
< 0.1%
539711 6
 
< 0.1%
539729 6
 
< 0.1%
539759 6
 
< 0.1%
Other values (14493) 85062
99.9%
ValueCountFrequency (%)
483129 6
< 0.1%
483130 6
< 0.1%
483131 6
< 0.1%
483132 4
< 0.1%
483133 6
< 0.1%
483134 6
< 0.1%
483135 6
< 0.1%
483136 6
< 0.1%
483137 6
< 0.1%
483138 6
< 0.1%
ValueCountFrequency (%)
2118418 6
< 0.1%
2060645 6
< 0.1%
2060644 6
< 0.1%
2060643 6
< 0.1%
2060642 6
< 0.1%
2060641 6
< 0.1%
2060640 6
< 0.1%
2060639 6
< 0.1%
2060638 6
< 0.1%
2060637 6
< 0.1%

homeTeamID
Real number (ℝ)

Distinct164
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9356.3284
Minimum4087
Maximum208931
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:46:59.239980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4087
5-th percentile8178
Q18535
median8686
Q39869
95-th percentile10260
Maximum208931
Range204844
Interquartile range (IQR)1334

Descriptive statistics

Standard deviation5639.3222
Coefficient of variation (CV)0.60272812
Kurtosis827.31043
Mean9356.3284
Median Absolute Deviation (MAD)509
Skewness27.14622
Sum7.9642939 × 108
Variance31801955
MonotonicityNot monotonic
2023-02-28T18:46:59.353004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8305 912
 
1.1%
8689 912
 
1.1%
9853 912
 
1.1%
9941 912
 
1.1%
9864 912
 
1.1%
9847 912
 
1.1%
8456 912
 
1.1%
8634 912
 
1.1%
8633 912
 
1.1%
9906 912
 
1.1%
Other values (154) 76002
89.3%
ValueCountFrequency (%)
4087 380
0.4%
4170 57
 
0.1%
6269 68
 
0.1%
6391 38
 
< 0.1%
7794 375
0.4%
7819 564
0.7%
7869 114
 
0.1%
7878 475
0.6%
7943 342
0.4%
8121 114
 
0.1%
ValueCountFrequency (%)
208931 38
 
< 0.1%
108893 114
 
0.1%
10281 456
0.5%
10278 95
 
0.1%
10269 810
1.0%
10268 228
 
0.3%
10267 912
1.1%
10261 798
0.9%
10260 906
1.1%
10252 912
1.1%

away_team_api_id
Real number (ℝ)

Distinct164
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9510.4273
Minimum4087
Maximum208931
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:46:59.472032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4087
5-th percentile8178
Q18534
median8686
Q39869
95-th percentile10260
Maximum208931
Range204844
Interquartile range (IQR)1335

Descriptive statistics

Standard deviation8065.376
Coefficient of variation (CV)0.84805611
Kurtosis506.51092
Mean9510.4273
Median Absolute Deviation (MAD)509
Skewness21.859425
Sum8.0954659 × 108
Variance65050290
MonotonicityNot monotonic
2023-02-28T18:46:59.586057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10252 912
 
1.1%
10194 912
 
1.1%
8456 912
 
1.1%
8455 912
 
1.1%
8668 912
 
1.1%
10260 912
 
1.1%
8586 912
 
1.1%
8650 912
 
1.1%
8472 906
 
1.1%
9825 906
 
1.1%
Other values (154) 76014
89.3%
ValueCountFrequency (%)
4087 446
0.5%
4170 108
 
0.1%
6269 104
 
0.1%
6391 110
 
0.1%
7794 439
0.5%
7819 550
0.6%
7869 109
 
0.1%
7878 561
0.7%
7943 329
0.4%
8121 106
 
0.1%
ValueCountFrequency (%)
208931 109
 
0.1%
108893 114
 
0.1%
10281 442
0.5%
10278 113
 
0.1%
10269 803
0.9%
10268 222
 
0.3%
10267 890
1.0%
10261 792
0.9%
10260 912
1.1%
10252 912
1.1%

B365H
Real number (ℝ)

Distinct118
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5767614
Minimum1.04
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:46:59.702083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.04
5-th percentile1.25
Q11.67
median2.1
Q32.75
95-th percentile5.75
Maximum26
Range24.96
Interquartile range (IQR)1.08

Descriptive statistics

Standard deviation1.7376252
Coefficient of variation (CV)0.67434463
Kurtosis23.325409
Mean2.5767614
Median Absolute Deviation (MAD)0.5
Skewness3.9216386
Sum219339.08
Variance3.0193413
MonotonicityNot monotonic
2023-02-28T18:46:59.822110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.1 4059
 
4.8%
2 3720
 
4.4%
1.91 2948
 
3.5%
2.5 2919
 
3.4%
2.2 2919
 
3.4%
1.44 2363
 
2.8%
2.4 2337
 
2.7%
2.25 2244
 
2.6%
2.3 2202
 
2.6%
1.8 1947
 
2.3%
Other values (108) 57464
67.5%
ValueCountFrequency (%)
1.04 30
 
< 0.1%
1.05 102
 
0.1%
1.06 126
 
0.1%
1.07 72
 
0.1%
1.08 168
0.2%
1.09 54
 
0.1%
1.1 204
0.2%
1.11 84
 
0.1%
1.13 300
0.4%
1.14 336
0.4%
ValueCountFrequency (%)
26 11
 
< 0.1%
23 5
 
< 0.1%
21 17
 
< 0.1%
19 21
 
< 0.1%
17 41
 
< 0.1%
15 135
0.2%
14 6
 
< 0.1%
13 235
0.3%
12 149
0.2%
11 132
0.2%

BWH
Real number (ℝ)

Distinct220
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5241906
Minimum1.03
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:46:59.941137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.03
5-th percentile1.25
Q11.67
median2.1
Q32.7
95-th percentile5.5
Maximum34
Range32.97
Interquartile range (IQR)1.03

Descriptive statistics

Standard deviation1.6079208
Coefficient of variation (CV)0.63700453
Kurtosis27.662383
Mean2.5241906
Median Absolute Deviation (MAD)0.5
Skewness3.9201569
Sum214864.15
Variance2.5854094
MonotonicityNot monotonic
2023-02-28T18:47:00.061163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 2827
 
3.3%
2.1 2591
 
3.0%
2.05 2554
 
3.0%
1.95 2505
 
2.9%
2.15 2291
 
2.7%
2.25 2167
 
2.5%
2.3 2079
 
2.4%
2.4 2039
 
2.4%
2.2 2037
 
2.4%
1.75 2007
 
2.4%
Other values (210) 62025
72.9%
ValueCountFrequency (%)
1.03 12
 
< 0.1%
1.04 6
 
< 0.1%
1.05 78
 
0.1%
1.06 90
 
0.1%
1.07 72
 
0.1%
1.08 198
0.2%
1.09 90
 
0.1%
1.1 168
0.2%
1.11 126
0.1%
1.12 240
0.3%
ValueCountFrequency (%)
34 6
 
< 0.1%
21 15
< 0.1%
19 6
 
< 0.1%
18 5
 
< 0.1%
17 11
< 0.1%
16.5 7
 
< 0.1%
16 12
< 0.1%
15.5 11
< 0.1%
15 18
< 0.1%
14.5 12
< 0.1%

IWH
Real number (ℝ)

Distinct146
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4392142
Minimum1.05
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:47:00.187192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.05
5-th percentile1.25
Q11.7
median2.1
Q32.6
95-th percentile5.1
Maximum20
Range18.95
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation1.420259
Coefficient of variation (CV)0.58226087
Kurtosis17.938802
Mean2.4392142
Median Absolute Deviation (MAD)0.45
Skewness3.4942613
Sum207630.79
Variance2.0171356
MonotonicityNot monotonic
2023-02-28T18:47:00.317220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 5315
 
6.2%
2.1 4970
 
5.8%
2.2 4647
 
5.5%
1.9 4088
 
4.8%
2.3 3906
 
4.6%
2.4 3219
 
3.8%
1.8 3052
 
3.6%
2.5 2729
 
3.2%
2.6 2629
 
3.1%
1.85 2331
 
2.7%
Other values (136) 48236
56.7%
ValueCountFrequency (%)
1.05 84
 
0.1%
1.07 198
 
0.2%
1.08 48
 
0.1%
1.1 276
0.3%
1.11 42
 
< 0.1%
1.12 360
0.4%
1.13 12
 
< 0.1%
1.15 510
0.6%
1.17 384
0.5%
1.18 42
 
< 0.1%
ValueCountFrequency (%)
20 11
 
< 0.1%
15 19
 
< 0.1%
14 50
0.1%
13 53
0.1%
12.5 21
 
< 0.1%
12 96
0.1%
11.5 12
 
< 0.1%
11 77
0.1%
10.5 30
 
< 0.1%
10.3 50
0.1%

LBH
Real number (ℝ)

Distinct119
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4959032
Minimum1.04
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:47:00.449251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.04
5-th percentile1.25
Q11.67
median2.1
Q32.62
95-th percentile5.5
Maximum26
Range24.96
Interquartile range (IQR)0.95

Descriptive statistics

Standard deviation1.5899034
Coefficient of variation (CV)0.63700525
Kurtosis22.841074
Mean2.4959032
Median Absolute Deviation (MAD)0.48
Skewness3.8426793
Sum212456.27
Variance2.5277929
MonotonicityNot monotonic
2023-02-28T18:47:00.574278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.1 3977
 
4.7%
2 3850
 
4.5%
2.2 3169
 
3.7%
2.25 2877
 
3.4%
1.8 2764
 
3.2%
2.5 2643
 
3.1%
1.91 2563
 
3.0%
2.4 2043
 
2.4%
1.83 1970
 
2.3%
2.38 1965
 
2.3%
Other values (109) 57301
67.3%
ValueCountFrequency (%)
1.04 18
 
< 0.1%
1.05 72
 
0.1%
1.06 36
 
< 0.1%
1.07 126
0.1%
1.08 150
0.2%
1.09 114
0.1%
1.1 198
0.2%
1.11 114
0.1%
1.12 216
0.3%
1.13 30
 
< 0.1%
ValueCountFrequency (%)
26 6
 
< 0.1%
23 5
 
< 0.1%
21 6
 
< 0.1%
19 14
 
< 0.1%
17 23
 
< 0.1%
15 79
0.1%
13 151
0.2%
12 166
0.2%
11 132
0.2%
10.5 6
 
< 0.1%

WHH
Real number (ℝ)

Distinct116
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5517918
Minimum1.02
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:47:00.691304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.02
5-th percentile1.25
Q11.7
median2.1
Q32.7
95-th percentile5.5
Maximum26
Range24.98
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6711369
Coefficient of variation (CV)0.65488763
Kurtosis23.643049
Mean2.5517918
Median Absolute Deviation (MAD)0.5
Skewness3.9555182
Sum217213.62
Variance2.7926984
MonotonicityNot monotonic
2023-02-28T18:47:00.812331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.5 3090
 
3.6%
2 3047
 
3.6%
2.1 2958
 
3.5%
2.3 2868
 
3.4%
2.4 2837
 
3.3%
2.2 2569
 
3.0%
1.91 2564
 
3.0%
2.05 2315
 
2.7%
2.25 2216
 
2.6%
1.44 2207
 
2.6%
Other values (106) 58451
68.7%
ValueCountFrequency (%)
1.02 6
 
< 0.1%
1.04 12
 
< 0.1%
1.05 54
 
0.1%
1.06 84
 
0.1%
1.07 42
 
< 0.1%
1.08 216
0.3%
1.1 270
0.3%
1.11 132
 
0.2%
1.12 156
 
0.2%
1.14 432
0.5%
ValueCountFrequency (%)
26 6
 
< 0.1%
23 5
 
< 0.1%
21 19
 
< 0.1%
19 11
 
< 0.1%
17 42
 
< 0.1%
15 146
0.2%
13 108
0.1%
12 219
0.3%
11 184
0.2%
10.5 12
 
< 0.1%

VCH
Real number (ℝ)

Distinct156
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6233912
Minimum1.03
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:47:00.930369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.03
5-th percentile1.25
Q11.7
median2.1
Q32.75
95-th percentile5.75
Maximum36
Range34.97
Interquartile range (IQR)1.05

Descriptive statistics

Standard deviation1.8911879
Coefficient of variation (CV)0.72089435
Kurtosis35.770315
Mean2.6233912
Median Absolute Deviation (MAD)0.5
Skewness4.6598604
Sum223308.3
Variance3.5765916
MonotonicityNot monotonic
2023-02-28T18:47:01.050396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.1 3205
 
3.8%
2 3035
 
3.6%
2.2 2755
 
3.2%
2.3 2346
 
2.8%
2.5 2341
 
2.8%
2.05 2320
 
2.7%
2.25 2194
 
2.6%
2.4 1803
 
2.1%
1.8 1795
 
2.1%
2.15 1787
 
2.1%
Other values (146) 61541
72.3%
ValueCountFrequency (%)
1.03 12
 
< 0.1%
1.04 30
 
< 0.1%
1.05 36
 
< 0.1%
1.06 174
0.2%
1.07 78
0.1%
1.08 54
 
0.1%
1.083 6
 
< 0.1%
1.09 138
0.2%
1.1 156
0.2%
1.11 108
0.1%
ValueCountFrequency (%)
36 6
 
< 0.1%
31 5
 
< 0.1%
29 11
 
< 0.1%
26 2
 
< 0.1%
23 11
 
< 0.1%
22 6
 
< 0.1%
21 23
< 0.1%
20 13
 
< 0.1%
19 41
< 0.1%
18 38
< 0.1%

avgOdds
Real number (ℝ)

Distinct2266
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5268982
Minimum1.042
Maximum28.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:47:01.175414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.042
5-th percentile1.258
Q11.682
median2.1
Q32.684
95-th percentile5.52
Maximum28.4
Range27.358
Interquartile range (IQR)1.002

Descriptive statistics

Standard deviation1.6247736
Coefficient of variation (CV)0.64299131
Kurtosis23.844992
Mean2.5268982
Median Absolute Deviation (MAD)0.478
Skewness3.8963364
Sum215094.63
Variance2.6398891
MonotonicityNot monotonic
2023-02-28T18:47:01.285449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.04 526
 
0.6%
2.05 515
 
0.6%
2.21 485
 
0.6%
2.2 474
 
0.6%
2.15 463
 
0.5%
2.12 457
 
0.5%
2.16 436
 
0.5%
2.13 420
 
0.5%
2.1 409
 
0.5%
2.09 403
 
0.5%
Other values (2256) 80534
94.6%
ValueCountFrequency (%)
1.042 12
< 0.1%
1.044 6
< 0.1%
1.048 6
< 0.1%
1.05 6
< 0.1%
1.052 12
< 0.1%
1.054 6
< 0.1%
1.054 6
< 0.1%
1.056 6
< 0.1%
1.056 6
< 0.1%
1.058 6
< 0.1%
ValueCountFrequency (%)
28.4 6
< 0.1%
22.4 5
< 0.1%
20.8 5
< 0.1%
20.4 6
< 0.1%
19.5 2
 
< 0.1%
19.4 6
< 0.1%
18.4 5
< 0.1%
17.1 5
< 0.1%
17 6
< 0.1%
16.7 6
< 0.1%

id_y
Real number (ℝ)

Distinct924
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean733.76216
Minimum10
Maximum1450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:47:01.411468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile85
Q1347
median777
Q31119
95-th percentile1376
Maximum1450
Range1440
Interquartile range (IQR)772

Descriptive statistics

Standard deviation424.13519
Coefficient of variation (CV)0.57802815
Kurtosis-1.2667832
Mean733.76216
Median Absolute Deviation (MAD)374
Skewness-0.055996648
Sum62459303
Variance179890.66
MonotonicityNot monotonic
2023-02-28T18:47:01.534494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
503 152
 
0.2%
1282 152
 
0.2%
504 152
 
0.2%
1311 152
 
0.2%
801 152
 
0.2%
802 152
 
0.2%
803 152
 
0.2%
804 152
 
0.2%
805 152
 
0.2%
806 152
 
0.2%
Other values (914) 83602
98.2%
ValueCountFrequency (%)
10 56
 
0.1%
11 56
 
0.1%
12 56
 
0.1%
13 56
 
0.1%
14 56
 
0.1%
15 56
 
0.1%
16 150
0.2%
17 150
0.2%
18 150
0.2%
19 150
0.2%
ValueCountFrequency (%)
1450 76
0.1%
1449 76
0.1%
1448 76
0.1%
1447 76
0.1%
1446 76
0.1%
1445 76
0.1%
1433 19
 
< 0.1%
1432 19
 
< 0.1%
1431 19
 
< 0.1%
1430 19
 
< 0.1%

buildUpPlaySpeed
Real number (ℝ)

Distinct56
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.730363
Minimum20
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:47:01.660522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile34
Q146
median54
Q364
95-th percentile70
Maximum80
Range60
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.711764
Coefficient of variation (CV)0.21797292
Kurtosis-0.39967199
Mean53.730363
Median Absolute Deviation (MAD)9
Skewness-0.37647729
Sum4573636
Variance137.16542
MonotonicityNot monotonic
2023-02-28T18:47:01.781551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 5332
 
6.3%
60 4734
 
5.6%
50 4686
 
5.5%
45 4255
 
5.0%
70 4068
 
4.8%
49 3968
 
4.7%
55 3550
 
4.2%
48 3499
 
4.1%
64 3155
 
3.7%
35 2857
 
3.4%
Other values (46) 45018
52.9%
ValueCountFrequency (%)
20 436
 
0.5%
23 19
 
< 0.1%
24 398
 
0.5%
25 304
 
0.4%
26 245
 
0.3%
28 114
 
0.1%
29 246
 
0.3%
30 1756
2.1%
31 272
 
0.3%
32 155
 
0.2%
ValueCountFrequency (%)
80 19
 
< 0.1%
78 284
 
0.3%
77 133
 
0.2%
76 363
 
0.4%
75 851
 
1.0%
74 134
 
0.2%
73 395
 
0.5%
72 428
 
0.5%
71 681
 
0.8%
70 4068
4.8%

buildUpPlayPassing
Real number (ℝ)

Distinct58
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.597531
Minimum20
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:47:01.907579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile30
Q138
median48
Q355
95-th percentile68
Maximum80
Range60
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.41386
Coefficient of variation (CV)0.23979942
Kurtosis-0.52304672
Mean47.597531
Median Absolute Deviation (MAD)8
Skewness0.20380629
Sum4051597
Variance130.2762
MonotonicityNot monotonic
2023-02-28T18:47:02.023605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 7836
 
9.2%
35 5282
 
6.2%
30 4719
 
5.5%
40 4195
 
4.9%
55 3983
 
4.7%
52 3855
 
4.5%
45 3288
 
3.9%
48 2824
 
3.3%
65 2800
 
3.3%
38 2768
 
3.3%
Other values (48) 43572
51.2%
ValueCountFrequency (%)
20 150
 
0.2%
22 136
 
0.2%
23 303
 
0.4%
24 245
 
0.3%
25 152
 
0.2%
26 416
 
0.5%
27 19
 
< 0.1%
28 136
 
0.2%
29 591
 
0.7%
30 4719
5.5%
ValueCountFrequency (%)
80 152
 
0.2%
79 38
 
< 0.1%
77 17
 
< 0.1%
75 190
 
0.2%
74 38
 
< 0.1%
73 570
 
0.7%
72 342
 
0.4%
71 102
 
0.1%
70 2414
2.8%
69 311
 
0.4%

chanceCreationPassing
Real number (ℝ)

Distinct49
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.902352
Minimum21
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:47:02.146633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile34
Q146
median52
Q362
95-th percentile70
Maximum80
Range59
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.250055
Coefficient of variation (CV)0.212657
Kurtosis-0.66446353
Mean52.902352
Median Absolute Deviation (MAD)8
Skewness-0.11501566
Sum4503154
Variance126.56374
MonotonicityNot monotonic
2023-02-28T18:47:02.260659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
50 6046
 
7.1%
49 5471
 
6.4%
52 4582
 
5.4%
55 4281
 
5.0%
70 4236
 
5.0%
65 4228
 
5.0%
48 3612
 
4.2%
68 3470
 
4.1%
53 3203
 
3.8%
60 2873
 
3.4%
Other values (39) 43120
50.7%
ValueCountFrequency (%)
21 136
 
0.2%
28 304
 
0.4%
30 2284
2.7%
31 133
 
0.2%
32 404
 
0.5%
33 128
 
0.2%
34 901
 
1.1%
35 2693
3.2%
36 762
 
0.9%
37 1407
1.7%
ValueCountFrequency (%)
80 152
 
0.2%
77 360
 
0.4%
76 145
 
0.2%
73 224
 
0.3%
72 1010
 
1.2%
71 602
 
0.7%
70 4236
5.0%
69 1368
 
1.6%
68 3470
4.1%
67 1381
 
1.6%

chanceCreationCrossing
Real number (ℝ)

Distinct55
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.398604
Minimum20
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:47:02.388686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile34
Q148
median54
Q364
95-th percentile70
Maximum80
Range60
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.489602
Coefficient of variation (CV)0.21121135
Kurtosis-0.4087076
Mean54.398604
Median Absolute Deviation (MAD)8
Skewness-0.29743051
Sum4630518
Variance132.01097
MonotonicityNot monotonic
2023-02-28T18:47:02.507714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 5275
 
6.2%
50 5241
 
6.2%
52 4995
 
5.9%
60 4823
 
5.7%
65 4713
 
5.5%
54 3990
 
4.7%
55 3303
 
3.9%
51 3054
 
3.6%
45 2836
 
3.3%
53 2549
 
3.0%
Other values (45) 44343
52.1%
ValueCountFrequency (%)
20 169
 
0.2%
23 38
 
< 0.1%
24 152
 
0.2%
25 190
 
0.2%
26 272
 
0.3%
27 38
 
< 0.1%
30 1243
1.5%
31 728
 
0.9%
33 307
 
0.4%
34 1961
2.3%
ValueCountFrequency (%)
80 300
 
0.4%
78 450
 
0.5%
77 318
 
0.4%
76 380
 
0.4%
75 148
 
0.2%
74 171
 
0.2%
73 800
 
0.9%
72 1204
 
1.4%
71 399
 
0.5%
70 5275
6.2%

chanceCreationShooting
Real number (ℝ)

Distinct54
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.578758
Minimum22
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:47:02.630742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile35
Q149
median54
Q364
95-th percentile70
Maximum80
Range58
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.068437
Coefficient of variation (CV)0.20279752
Kurtosis-0.36139293
Mean54.578758
Median Absolute Deviation (MAD)7
Skewness-0.21833654
Sum4645853
Variance122.51029
MonotonicityNot monotonic
2023-02-28T18:47:02.745767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 7752
 
9.1%
70 7116
 
8.4%
55 5322
 
6.3%
52 4797
 
5.6%
65 4317
 
5.1%
53 3174
 
3.7%
60 3049
 
3.6%
64 2957
 
3.5%
49 2774
 
3.3%
56 2741
 
3.2%
Other values (44) 41123
48.3%
ValueCountFrequency (%)
22 136
 
0.2%
23 301
 
0.4%
24 152
 
0.2%
29 263
 
0.3%
30 478
 
0.6%
31 151
 
0.2%
32 418
 
0.5%
33 130
 
0.2%
34 936
1.1%
35 1936
2.3%
ValueCountFrequency (%)
80 604
 
0.7%
79 152
 
0.2%
78 135
 
0.2%
77 38
 
< 0.1%
76 134
 
0.2%
75 340
 
0.4%
73 539
 
0.6%
72 1106
 
1.3%
71 253
 
0.3%
70 7116
8.4%

defencePressure
Real number (ℝ)

Distinct48
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.709652
Minimum23
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:47:02.863794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile30
Q139
median46
Q354
95-th percentile66
Maximum72
Range49
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.469532
Coefficient of variation (CV)0.22414066
Kurtosis-0.30892706
Mean46.709652
Median Absolute Deviation (MAD)7
Skewness0.31831579
Sum3976019
Variance109.61111
MonotonicityNot monotonic
2023-02-28T18:47:02.981820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
45 7412
 
8.7%
35 5005
 
5.9%
47 4541
 
5.3%
50 3992
 
4.7%
30 3917
 
4.6%
70 3635
 
4.3%
55 3402
 
4.0%
49 3181
 
3.7%
43 3155
 
3.7%
40 3051
 
3.6%
Other values (38) 43831
51.5%
ValueCountFrequency (%)
23 450
 
0.5%
24 55
 
0.1%
25 459
 
0.5%
26 313
 
0.4%
27 168
 
0.2%
28 236
 
0.3%
29 266
 
0.3%
30 3917
4.6%
31 300
 
0.4%
32 186
 
0.2%
ValueCountFrequency (%)
72 136
 
0.2%
70 3635
4.3%
68 305
 
0.4%
67 152
 
0.2%
66 438
 
0.5%
65 1465
1.7%
64 879
 
1.0%
63 893
 
1.0%
62 266
 
0.3%
61 892
 
1.0%

defenceAggression
Real number (ℝ)

Distinct46
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.087075
Minimum27
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:47:03.102849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile34
Q144
median49
Q356
95-th percentile70
Maximum72
Range45
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.712331
Coefficient of variation (CV)0.19390893
Kurtosis-0.30067391
Mean50.087075
Median Absolute Deviation (MAD)6
Skewness0.22581158
Sum4263512
Variance94.329373
MonotonicityNot monotonic
2023-02-28T18:47:03.212873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
45 8291
 
9.7%
55 5188
 
6.1%
70 4915
 
5.8%
50 4872
 
5.7%
47 4707
 
5.5%
44 3663
 
4.3%
48 3404
 
4.0%
60 3375
 
4.0%
52 2984
 
3.5%
40 2908
 
3.4%
Other values (36) 40815
47.9%
ValueCountFrequency (%)
27 57
 
0.1%
28 114
 
0.1%
29 17
 
< 0.1%
30 2602
3.1%
31 152
 
0.2%
32 154
 
0.2%
33 322
 
0.4%
34 1233
1.4%
35 1309
1.5%
36 17
 
< 0.1%
ValueCountFrequency (%)
72 136
 
0.2%
71 134
 
0.2%
70 4915
5.8%
69 134
 
0.2%
68 229
 
0.3%
67 851
 
1.0%
66 429
 
0.5%
65 2581
3.0%
64 186
 
0.2%
63 813
 
1.0%

defenceTeamWidth
Real number (ℝ)

Distinct42
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.414946
Minimum29
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-02-28T18:47:03.330899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile35
Q148
median52
Q358
95-th percentile70
Maximum73
Range44
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.307538
Coefficient of variation (CV)0.17757412
Kurtosis-0.21973887
Mean52.414946
Median Absolute Deviation (MAD)5
Skewness-0.060153316
Sum4461665
Variance86.630264
MonotonicityNot monotonic
2023-02-28T18:47:03.436923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
50 9186
 
10.8%
65 5418
 
6.4%
51 4814
 
5.7%
55 4723
 
5.5%
49 4439
 
5.2%
70 4135
 
4.9%
53 3709
 
4.4%
52 3684
 
4.3%
54 3365
 
4.0%
45 3346
 
3.9%
Other values (32) 38303
45.0%
ValueCountFrequency (%)
29 102
 
0.1%
30 1627
1.9%
32 173
 
0.2%
33 74
 
0.1%
34 316
 
0.4%
35 2357
2.8%
36 528
 
0.6%
37 440
 
0.5%
38 1017
1.2%
39 1222
1.4%
ValueCountFrequency (%)
73 135
 
0.2%
70 4135
4.9%
69 474
 
0.6%
68 1613
 
1.9%
67 727
 
0.9%
66 588
 
0.7%
65 5418
6.4%
64 911
 
1.1%
63 589
 
0.7%
62 775
 
0.9%

Interactions

2023-02-28T18:46:54.209052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:05.480895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:07.940470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:10.678089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:13.251653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:15.694202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:18.110757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:20.793418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:23.328000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:25.949678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:28.358220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:30.871803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:33.335357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:36.113982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:38.613544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:41.151126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:43.680684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:46.639351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:49.181921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:51.732495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:54.328079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:05.613924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:08.063497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:10.806103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:13.370680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:15.807228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:18.228783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:20.920446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:23.441025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:26.063703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:28.476247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:30.987828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:33.458384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:36.233008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:38.733571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:41.271153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:43.811713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:46.771379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:49.300948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:51.851522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:54.460109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:05.744954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:08.208519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:10.940147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:13.500720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:15.935268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:18.361813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:21.057477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:23.570054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:26.193733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:28.613277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:31.117858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:33.595415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:36.368039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:38.869602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:41.410184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:43.954745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:46.909410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:49.447982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:51.982551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:54.582136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:05.862981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:08.332546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:11.054159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:13.616750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:16.049293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:18.481841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:21.177504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:23.685080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:26.308759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:28.734310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:31.233884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:33.715442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:36.488066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:38.990629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:41.537202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:44.083775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:47.032438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:49.567008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:52.099578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:54.700163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:05.984008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:08.582604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:11.169186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:13.732772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:16.170337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:18.601867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:21.299531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:23.800106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:26.425785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:28.856338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:31.350910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:34.098529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:36.610093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:39.111656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:41.659229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:44.540878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:47.154465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:49.688036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:52.218605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:54.819190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:06.108036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:08.711632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-02-28T18:46:10.022927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:12.515498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:15.083065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:17.512622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:20.169278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:22.702859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:25.146497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:27.764086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:30.246662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:32.720218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:35.489841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:37.992405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:40.509970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:43.048542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:46.002207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:48.557781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:51.060344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:53.596915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:56.171494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:07.468363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:10.154956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:12.640526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:15.209093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:17.636651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:20.299318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:22.831888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:25.269525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:27.888114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:30.373691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:32.846247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:35.618871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:38.120433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:40.643001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:43.176570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:46.133235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:48.685810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:51.187372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:53.720942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:56.296522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:07.592395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:10.286986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:12.763554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:15.334121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:17.760678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:20.427335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:22.960917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:25.393553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:28.010141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:30.502721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:32.978276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:35.749900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:38.249463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:40.776031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:43.304599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:46.262265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:48.812838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:51.336406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:53.852973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:56.414548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:07.708421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:10.412014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:12.880580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:15.451148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:17.877704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:20.548362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:23.083945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:25.718626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:28.125167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:30.625747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:33.097303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:35.871927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:38.371490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:40.899059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:43.426627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:46.385293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:48.934876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:51.474437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:53.973999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:56.531574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:07.824443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:10.542044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:12.997606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:15.569174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:17.993731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:20.668400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:23.205972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:25.833652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:28.242194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:30.745776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:33.216331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:35.992955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:38.492517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:41.026087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:43.549654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:46.513321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:49.058904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:51.604467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T18:46:54.091025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-02-28T18:47:03.559553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
id_xmatch_api_idhomeTeamIDaway_team_api_idB365HBWHIWHLBHWHHVCHavgOddsid_ybuildUpPlaySpeedbuildUpPlayPassingchanceCreationPassingchanceCreationCrossingchanceCreationShootingdefencePressuredefenceAggressiondefenceTeamWidthcountry_idleague_idseason
id_x1.0000.292-0.087-0.085-0.036-0.022-0.024-0.026-0.028-0.026-0.025-0.069-0.178-0.2240.128-0.0730.1330.0760.0350.1400.8600.8600.252
match_api_id0.2921.000-0.021-0.0260.0210.0450.0360.0540.0580.0540.049-0.0020.000-0.0450.009-0.0280.0270.002-0.0020.0110.0000.0001.000
homeTeamID-0.087-0.0211.000-0.029-0.051-0.054-0.057-0.057-0.054-0.053-0.0550.192-0.008-0.017-0.0240.017-0.071-0.033-0.067-0.0710.0590.0590.078
away_team_api_id-0.085-0.026-0.0291.0000.0560.0560.0600.0560.0540.0530.0560.0030.0130.016-0.0160.003-0.008-0.011-0.002-0.0100.0710.0710.096
B365H-0.0360.021-0.0510.0561.0000.9950.9900.9930.9950.9960.9970.0340.0310.182-0.0390.011-0.131-0.176-0.060-0.0670.0780.0780.034
BWH-0.0220.045-0.0540.0560.9951.0000.9910.9930.9940.9950.9980.0330.0300.177-0.0380.010-0.129-0.172-0.059-0.0660.0800.0800.028
IWH-0.0240.036-0.0570.0600.9900.9911.0000.9900.9880.9880.9940.0340.0280.176-0.0390.010-0.130-0.169-0.057-0.0660.0880.0880.040
LBH-0.0260.054-0.0570.0560.9930.9930.9901.0000.9940.9940.9970.0360.0290.180-0.0380.013-0.129-0.174-0.060-0.0660.0810.0810.038
WHH-0.0280.058-0.0540.0540.9950.9940.9880.9941.0000.9960.9980.0340.0300.181-0.0390.011-0.129-0.175-0.060-0.0670.0820.0820.039
VCH-0.0260.054-0.0530.0530.9960.9950.9880.9940.9961.0000.9980.0340.0300.181-0.0390.011-0.129-0.175-0.061-0.0670.0800.0800.036
avgOdds-0.0250.049-0.0550.0560.9970.9980.9940.9970.9980.9981.0000.0340.0300.179-0.0390.011-0.130-0.174-0.060-0.0670.0850.0850.034
id_y-0.069-0.0020.1920.0030.0340.0330.0340.0360.0340.0340.0341.000-0.0140.0100.0920.1410.0270.010-0.0340.0060.3050.3050.052
buildUpPlaySpeed-0.1780.000-0.0080.0130.0310.0300.0280.0290.0300.0300.030-0.0141.0000.2900.2710.1870.152-0.0050.130-0.0680.2380.2380.019
buildUpPlayPassing-0.224-0.045-0.0170.0160.1820.1770.1760.1800.1810.1810.1790.0100.2901.0000.1910.238-0.096-0.1730.050-0.0530.2400.2400.022
chanceCreationPassing0.1280.009-0.024-0.016-0.039-0.038-0.039-0.038-0.039-0.039-0.0390.0920.2710.1911.0000.2440.1430.1910.1590.0390.2760.2760.018
chanceCreationCrossing-0.073-0.0280.0170.0030.0110.0100.0100.0130.0110.0110.0110.1410.1870.2380.2441.0000.0090.0380.0810.0450.2330.2330.025
chanceCreationShooting0.1330.027-0.071-0.008-0.131-0.129-0.130-0.129-0.129-0.129-0.1300.0270.152-0.0960.1430.0091.0000.1760.0990.1460.1940.1940.030
defencePressure0.0760.002-0.033-0.011-0.176-0.172-0.169-0.174-0.175-0.175-0.1740.010-0.005-0.1730.1910.0380.1761.0000.2560.3840.2600.2600.020
defenceAggression0.035-0.002-0.067-0.002-0.060-0.059-0.057-0.060-0.060-0.061-0.060-0.0340.1300.0500.1590.0810.0990.2561.0000.0560.2180.2180.021
defenceTeamWidth0.1400.011-0.071-0.010-0.067-0.066-0.066-0.066-0.067-0.067-0.0670.006-0.068-0.0530.0390.0450.1460.3840.0561.0000.2760.2760.024
country_id0.8600.0000.0590.0710.0780.0800.0880.0810.0820.0800.0850.3050.2380.2400.2760.2330.1940.2600.2180.2761.0001.0000.000
league_id0.8600.0000.0590.0710.0780.0800.0880.0810.0820.0800.0850.3050.2380.2400.2760.2330.1940.2600.2180.2761.0001.0000.000
season0.2521.0000.0780.0960.0340.0280.0400.0380.0390.0360.0340.0520.0190.0220.0180.0250.0300.0200.0210.0240.0000.0001.000

Missing values

2023-02-28T18:46:56.724618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-28T18:46:57.167731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

id_xcountry_idleague_idseasondatematch_api_idhomeTeamIDaway_team_api_idB365HBWHIWHLBHWHHVCHavgOddsid_ybuildUpPlaySpeedbuildUpPlayPassingchanceCreationPassingchanceCreationCrossingchanceCreationShootingdefencePressuredefenceAggressiondefenceTeamWidth
01729172917292008/20092008-08-17 00:00:0048904210260102611.291.31.31.251.251.281.2768077045457065405040
11729172917292008/20092008-08-17 00:00:0048904210260102611.291.31.31.251.251.281.2768086540656570454565
21729172917292008/20092008-08-17 00:00:0048904210260102611.291.31.31.251.251.281.2768094654466055405056
31729172917292008/20092008-08-17 00:00:0048904210260102611.291.31.31.251.251.281.2768104638466837494956
41729172917292008/20092008-08-17 00:00:0048904210260102611.291.31.31.251.251.281.2768114654497256424156
51729172917292008/20092008-08-17 00:00:0048904210260102611.291.31.31.251.251.281.2768123844494440545356
61739172917292008/20092008-10-29 00:00:004891321026086541.201.21.21.201.201.201.2008077045457065405040
71739172917292008/20092008-10-29 00:00:004891321026086541.201.21.21.201.201.201.2008086540656570454565
81739172917292008/20092008-10-29 00:00:004891321026086541.201.21.21.201.201.201.2008094654466055405056
91739172917292008/20092008-10-29 00:00:004891321026086541.201.21.21.201.201.201.2008104638466837494956
id_xcountry_idleague_idseasondatematch_api_idhomeTeamIDaway_team_api_idB365HBWHIWHLBHWHHVCHavgOddsid_ybuildUpPlaySpeedbuildUpPlayPassingchanceCreationPassingchanceCreationCrossingchanceCreationShootingdefencePressuredefenceAggressiondefenceTeamWidth
851122452921518215182015/20162015-10-03 00:00:002030143830683722.12.102.12.02.12.052.076684844534253533957
851132452921518215182015/20162015-10-03 00:00:002030143830683722.12.102.12.02.12.052.076694844534253533957
851142452921518215182015/20162015-10-03 00:00:002030143830683722.12.102.12.02.12.052.076704844534253533957
851152452921518215182015/20162015-10-03 00:00:002030143830683722.12.102.12.02.12.052.076714844534253533957
851162455021518215182015/20162015-10-25 00:00:0020301648306102053.53.253.33.53.53.603.436663030454070354545
851172455021518215182015/20162015-10-25 00:00:0020301648306102053.53.253.33.53.53.603.436675456676159485667
851182455021518215182015/20162015-10-25 00:00:0020301648306102053.53.253.33.53.53.603.436684844534253533957
851192455021518215182015/20162015-10-25 00:00:0020301648306102053.53.253.33.53.53.603.436694844534253533957
851202455021518215182015/20162015-10-25 00:00:0020301648306102053.53.253.33.53.53.603.436704844534253533957
851212455021518215182015/20162015-10-25 00:00:0020301648306102053.53.253.33.53.53.603.436714844534253533957